Intelligent Target Detection Method for HFSWR Based on Dual-Scale Branch Fusion Network and Adaptive Threshold Control

Abstract

High-frequency surface wave radar (HFSWR) is a crucial tool for oceanic remote sensing and surveillance; however, radar target detection is challenged by the presence of background clutter and interference. In response, this article designs a novel dual-scale branch fusion network specifically for detecting target signals in the range-Doppler (RD) spectrum. The network effectively enhances the ability to distinguish between targets and clutter by combining large-scale environmental feature sensing with small-scale target signal structure analysis. Additionally, we propose a novel detection threshold adjustment mechanism based on the RD spectrum perception network. First, an initial detection threshold is calculated using the traditional constant false alarm rate (CFAR) method. Then, the output of the softmax layer in the RD spectrum perception network is used to adjust the threshold, improving the robustness and accuracy of the detection process. The RD spectrum perception network is trained jointly using data from the Automatic Identification System (AIS) associated with HFSWR and simulated target-embedded data. Multiple validations and analyses of the proposed detection method are conducted with these datasets. Experimental results demonstrate that the proposed method has good detection performance, outperforming several other existing methods.